Plasma chemokines CXCL10 and CXCL9 as potential diagnostic markers of drug-sensitive and drug-resistant tuberculosis

Tuberculosis (TB) diagnosis still remains to be a challenge with the currently used immune based diagnostic methods particularly Interferon Gamma Release Assay due to the sensitivity issues and their inability in differentiating stages of TB infection. Immune markers are valuable sources for understanding disease biology and are easily accessible. Chemokines, the stimulant, and the shaper of host immune responses are the vital hub for disease mediated dysregulation and their varied levels in TB disease are considered as an important marker to define the disease status. Hence, we wanted to examine the levels of chemokines among the individuals with drug-resistant, drug-sensitive, and latent TB compared to healthy individuals. Our results demonstrated that the differential levels of chemokines between the study groups and revealed that CXCL10 and CXCL9 as potential markers of drug-resistant and drug-sensitive TB with better stage discriminating abilities.

In continuum with this, we hypothesized that the behaviour of chemokines and their release in the host's circulation during infection may vary between different forms of TB. In addition, their differential levels can reveal their biomarker efficiency in distinguishing TB from LTB or DR-TB. Therefore, we intended to estimate the circulating levels of C-C and C-X-C chemokine ligands by multiplex assay across the spectrum of TB infection (LTB, DS-TB, and DR-TB). Our results demonstrated the differences in the chemokine profile between the study groups. In addition, our results divulged the set of chemokines that could effectively discriminate HC, LTB, DS-TB, and DR-TB groups. Thus, our study aid in understanding the host immunological response in chemokine secretion towards different spectra of TB infection and deciphered the chemokine signature as potent biomarker targets.

Methods
Ethical approval and informed consent. The study was approved by the National Institute for Research in Tuberculosis Ethical committee (NIRT, IEC 2015022), Chennai, India. Informed consent was obtained from all the recruited individuals. All the experiments were performed in accordance with relevant guidelines and regulations.
Study population. The study population consists of healthy controls (HC) (n = 40), latently infected individuals (LTB) (n = 40), drug-sensitive TB (DS-TB) (n = 40) and drug-resistant TB (DR-TB) (n = 40). The study cohort was same from our previous paper with detailed information of study population and sample characteristics 28 . Our cohort composed of adult participants aged above 18 and below 65 and are exclusive of other infections and co-morbid conditions. Clinically well characterized cohort that has been diagnosed for TB has been included in our study which are exclusive of other infections and other co-morbid conditions like Diabetes Mellitus, HIV, HCV and HBV. Blood was collected at one-time point from the DS-TB/DR-TB groups before starting treatment. Plasma was separated by centrifuging blood at 2600 rpm for 10 min and stored at − 80 °C until further assays.
Statistical analysis. Graph-Pad PRISM Version 9.0 (GraphPad Software, CA, UA) was used to analyse the statistical difference among the groups. R software version 4.2.0 (R Core Team, 2022) was used to perform random forest analysis and principal component analysis. The Shapiro-Wilk normality test was performed to test the normality of the data. Chemokine concentrations are shown as median and interquartile ranges (IQR). Statistical significance between the study groups (DR-TB, DS-TB, LTB, and HC) for chemokine observations were analysed using the Dunn test corrected for multiple comparisons using Bonferroni's test. Sensitivity and specificity were assessed using receiver operating curve (ROC) analysis. The importance of the chemokines was ranked through random forest (RF) analysis. The dimensional reduction was carried out using principal component analysis (PCA) to identify the classification pattern of the ranked chemokines. In order to measure the degree of association between the chemokines, Spearman coefficients were calculated. Hierarchical clustering was performed to visualize the segmentation of these chemokines in the study groups using the SOM module in the Multi-experiment Viewer Application (http:// www. tm4. org/). p < 0.05 was considered statistically significant.

Results
Basic characteristics. The demographics and haematological data of the study participants with their significance values are described in detail in our previous paper 28  Drug-resistant tuberculosis is associated with increased levels of chemokines. We wanted to determine the dynamics of chemokines at the different spectra of TB disease and/or infection may therefore be useful as potential biomarker targets for diagnosis. We examined an array of CC and CXC chemokines using multiplex assay profiles in plasma of drug-resistant (DR-TB), drug-sensitive (DS-TB), and LTB and compared them with healthy controls. Chemokine concentration was shown as median and IQR in Table 1. As shown in Fig. 1, DR-TB exhibited significantly increased CC chemokines CCL2 (p = 0.0492), CXC chemokines CXCL9 (p = 0.376) and CXCL10 (p = 0.0317) in comparison to DS-TB.
Further, DR-TB patients exhibited significantly increased levels of CC chemokines CCL1 (p < 0.0001), CCL2 (p = 0.0012), CCL3 (p < 0.0001), CXC chemokines CXCL1 (p < 0.0001), CXCL9 (p < 0.0001) and CXCL10 (p < 0.0001) in comparison to LTB individuals. DR-TB patients exhibited significantly higher levels of CCL1 (p < 0.0001), CCL2 (p < 0.0001), CCL3 (p = 0.0002), CXC chemokines CXCL1 (p < 0.0001), CXCL9 (p < 0.0001), CXCL10 (p < 0.0001) and CXCL11 (p < 0.0001) in comparison to the control group of individuals. DS-TB exhibited significantly higher levels of CCL1 (p = 0.0036), CCL3 (p = 0.0486), CXCL1 (p < 0.0001), CXCL9 (p < 0.0001) and CXCL10 (p < 0.0001) in comparison to individuals with LTB. DS-TB exhibited significantly higher levels of CCL1 (p < 0.0001), CCL2 (p = 0.0054), CXCL1 (p < 0.0001), CXCL9 (p < 0.0001), CXCL10 (p < 0.0001) and Heatmaps divulge tendencies in the chemokine milieu in DR-TB, DS-TB, LTB, and HC. The trends in the chemokine expression profile were assessed by hierarchical clustering of chemokines using normalized values. For this, the raw individual chemokine expression counts were transformed to log 2 values and normalized with group mean value of HC for respective chemokine across all the samples. The normalized counts were shown in Fig. 2, and the color panel of the heat map reveals the serial increase of chemokines (both numbers and levels) from latency (black or blue) to drug-sensitive (blue, green, and red) and to drug-resistant TB (blue, green, orange, and red). Before infection, the latent condition presented the chemokine panel with near-high levels of CXCL11 and mild or moderate levels of CXCL2, CCL4, CCL2, CCL1, and CXCL9. In the diseased state, DS-TB individuals presented differential chemokine expression with high levels of CXCL1 and CXCL10; near high levels of CCL11, CCL2 and CXCL11; moderate levels of CCL1 and CXCL9 and mild levels of CXCL1, CCL4 and CCL3. DR-TB individuals are associated with abundant chemokine expression with high levels of CXCL1 and CXCL10; near high levels of CXCL2, CCL11, CCL3, CCL2, CCL1, CXCL9 and CXCL11 and    In addition, we performed a random forest (RF) analysis to understand the importance of these chemokines and their distinguishing ability toward the separation of study groups. According to the order of importance, RF plots of overall comparison (HC vs LTB vs DS-TB vs DR-TB) presented CXCL9, CXCL10, and CXCL1 as the top classifiers (Fig. 4A). This was in accordance with the ROC results where these chemokines displayed higher AUC values of above 0.8. Similarly in the subgroup comparisons, the same CXCL9 was obtained as the top classifier for HC vs LTB/DS-TB/DR-TB (Fig. 5A-1-A-3) whereas, CXCL10 for LTB vs DS-TB/DR-TB (Fig. 5A-4,A-5) and DS-TB vs DR-TB (Fig. 5A-6).
All chemokine variables were then dimensionally reduced through the principal component analysis, resulting in a lower variation of the first two dimensions, and the ellipses of HC overlapped within LTB and DS-TB within DR-TB. To achieve better bifurcation with a minimum of 80% variance, the weaker chemokines from the RF plots were removed and the dimensionality reduction analysis was repeated. The PCA of the top 3 chemokines (CXCL9, CXCL10 and CXCL1) exhibited better separation of clusters (LTB, DS-TB, and DR-TB) with variances above 80% (Fig. 4B) (Fig. 5B-1-B-6).

Discussion
Understanding the host immune response upon infection is a key for unlocking disease pathogenesis and recognizing the protective or pathological linkers. During infection, the coaction of inflammatory signals and chemokines determines their levels and timing of production, thus mediating protection or causing damage to the host 7,25 . MTB infection rooted higher levels of chemokines in the circulation of PTB patients 25 and their circulating levels are described as discriminators for LTB and active TB 29 . However, the nature of individuals infected with DR-TB is less focused. Our hypothesis is that individuals with DR-TB may have different chemokine profiles compared to those with DS-TB. To understand this, we considered having an overview of C-C and C-X-C chemokines across the TB spectrum (LTB, DS-TB, and DR-TB) and healthy individuals, thereby identifying biomarker targets, especially between DR-TB vs DS-TB, DS-TB vs LTB and LTB vs HC comparisons. Our results demonstrated two main findings: (i) differential levels between groups (moderate increase in LTB, high in DS-TB, and extremely high in DR-TB) compared to HC and (ii) chemokines (CXCL10, CXCL9 and CXCL1) as the promising biomarker signature. These findings correlate with the previous postulations as both CXCL10 and CXCL9 and their elevated levels has been considered as potent diagnostic markers for both pulmonary and extrapulmonary TB 2,25,30,31 . Decades of research have been invested in CXCL10 to outperform the sensitivity issues faced through IGRA. CXCL10 are induced by antigen-presenting cells (APC) and stimulated macrophages during infection that assist chemotaxis, leukocyte migration, cell growth and angiogenesis and recent data determined that it could also restrict MTB replication [32][33][34][35][36][37] . CXCL10/IP-10 alone or in combination with acute phase proteins or cytokines are proposed as markers of bacterial burden 25 , culture conversion 25,38 , LTB and active TB discrimination 23,37 , treatment response 27,34,38,39 , childhood TB diagnosis 40 and triage test for TB diagnosis 41 . Ferrian et al., reported lower CXCL10 levels in DR-TB that contrasted the previous reports from DS-TB and the authors claimed it as immunological suppression due to continuous TB exposure and re-treatment 38 . However, in our study DR-TB samples are of a similar kind, which are previously treated for TB but had strikingly higher CXCL10 than DS-TB, LTB, and HC individuals. This could possibly be due to the infiltrating APCs and associated hyper-inflammation that aids disease severity. Furthermore, the disease-mediated elevation of CXCL10 is evident, as it stands out as the top classifier for DR-TB vs DS-TB/LTB and DS-TB vs LTB.
CXCL9 came out as the topmost for discriminating DR-TB/DS-TB from HC. CXCL9 is an IFN-gammainduced chemokine that is predominantly elevated in diseased groups in our study. These are crucial drivers for  44 . The other promising candidates reported were CCL1, CCL3, and CXCL1 25 . Among which, CXCL1 emerged as a potential candidate that effectively discriminates active TB from LTB, healthy and non-TB lung disease and successfully met the WHO's target product profile (TPP) criteria 45 . Although CXCL1 emerges one among the top 3 chemokines in our study, it only had the statistical significance to distinguish the diseased state (DS-TB/DR-TB) from healthy or LTB and did not discriminate DR-TB from DS-TB or LTB from HC. Our data revealed that most of the estimated chemokines CXCL11, CXCL10, CXCL9, CXCL1, CCL3, CCL2, and CCL1 were remarkably higher in the DR-TB and DS-TB groups. Thus, many chemokines invariably extend their association towards disease burden with good diagnostic abilities with profound AUC values greater than 0.8 between various group comparisons. Nevertheless, only a few were able to differentiate DR-TB from DS-TB. Interestingly, Guzman et al., stated the probable differences between MDR-TB and DS-TB are due to the expression pattern of chemokine receptors (CCR2 and CCR4) in monocytes that control the kinetics of immune cell migration and recruitment rather than chemokine ligands. They also observed a continued increase of CD3+ monocytes, CCR4+ monocytes, CXCR1+ and CXCR3+ T cells in the circulation of MDR-TB patients even after anti-TB treatment that could aid chronicity of infection and delay in recovery 46 . Being a cross-sectional approach, our study lacked the follow-up data that could promptly help to identify biomarkers for culture conversion, bacterial burden, treatment response and outcome. In addition to this, the investigated panel had limited chemokines and other prominent chemokines (for example CCL-5) are being missed out. However, on c. d. www.nature.com/scientificreports/ a positive side, our study had an appreciable sample size with the inclusion of different spectra of TB infection/ disease (LTB, DS-TB, and DR-TB groups) that could briefly suggest the chemokine signature with their diagnostic abilities. To conclude, CXCL10 and CXCL9 emerged as signatures for drug-sensitive and drug-resistant TB. Further extending the chemokine panel with longitudinal and functional studies may enable the true candidates to diagnose different TB infection stages.

Data availability
The data supporting the findings of this article will be made available by the corresponding author, upon request.